检索结果-内蒙古大学图书馆

27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Chen, Feng Wang, Li Zhang, Jiawei Sun, Qi Lai, Pinyan State Grid Zhejiang Elect Power CO LTD Mat Branch Quzhou Zhejiang Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350349184;9798350349191

This research introduces an advanced attention-driven model designed to optimize warehouse order-picking, an enhancement of the foundational work by Kool et al. (2018). Fundamentally, our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem of order-picking optimization, leveraging the latest in attention-based deep learning and reinforcement learning techniques. Compared to existing methods, our innovative model shows superior performance, offering significant potential in warehousing solutions.

关键词： order picking attention model storage selection reinforcement learning encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

引用

INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2023年 1-12页

作者： Roy, Sunita Mehera, Ranjan Pal, Rajat Kumar Bandyopadhyay, Samir Kumar Univ Calcutta Dept Comp Sci & Engn JD-2Sect 3Salt Lake Kolkata 700106 West Bengal India Anodot Inc 20130 Lakeview Ctr PlSuite 400 Ashburn VA 20147 USA Bhawanipur Educ Soc Coll 5Elgin RdSreepallyBhowanipore Kolkata 700020 West Bengal India

Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machine learning experts. Although a generic model can be used in the search for a near-optimal solution in any problem domain, what makes these DL models context-sensitive is the combination of the training data and the hyperparameters. Due to the lack of inherent explainability of DL models, the hyperparameter optimization (HPO) or tuning specific to each model is a combination of art, science, and experience. In this article, we have explored various existing methods or ways to identify the optimal set of values for the hyperparameters specific to the DL models along with the techniques to realize those methods in real-life situations. The article also includes a detailed comparative study among various state-of-the-art HPO techniques using the Keras Tuner tuning toolkit and highlights the observations describing how the model performance can be improved by applying various HPO techniques.

关键词： Automated machine learning Brain tumour segmentation Deep learning encoder-decoder Hyperparameter optimization SA-Net

来源：评论

学校读者我要写书评

暂无评论

GRAN: graph recurrent attention network for pedestrian orientation classification

引用

COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 891-908页

作者： Li, Xiao Ma, Shexiang Shan, Liqing Liu, Sheng Chai, Song Li, Xiao Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin 300384 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 211189 Peoples R China

In complex traffic scenes, accurate identification of pedestrian orientations can help drivers determine pedestrian trajectories and help reduce traffic accidents. However, there are still many challenges in pedestrian orientation recognition. First, due to the irregular appearance of pedestrians, it is difficult for general Convolutional Neural Networks (CNNs) to extract discriminative features. In addition, more features of body parts help to judge the orientation of pedestrians. For example, head, arms and legs. However, they are usually small and not conducive to feature extraction. Therefore, in this work, we use several discrete values to define the orientation of pedestrians, and propose a Gated Graph Neural Network (GGNN)-based Graph Recurrent Attention Network (GRAN) to classify the orientation of pedestrians. The contributions are as follows: (1) We construct a body parts graph consisting of head, arms and legs on the feature maps output by the CNN backbone. (2) Mining the dependencies between body parts on the graph via the proposed GRAN, and utilizing the encoder-decoder to propagate features among graph nodes. (3) In this process, we propose an adjacency matrix with attention edge weights to dynamically represent graph node relationships, and the edge weights are learned during network training. To evaluate the proposed method, we conduct experiments on three different benchmarks (PDC, PDRD, and Cityscapes) with 8, 3, and 4 orientations, respectively. Note that the orientation labels for PDRD and Cityscapes are annotated by our hand. The proposed method achieves 97%, 91% and 90% classification accuracy on the three data sets, respectively. The results are all higher than current state-of-the-art methods, which demonstrate the effectiveness of the proposed method.

关键词： Pedestrian orientation Body parts Graph Attention encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Satellite imagery analysis for road segmentation using U-Net architecture

引用

JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12710-12725页

作者： Chaudhary, Vidhi Buttar, Preetpal Kaur Sachan, Manoj Kumar St Longowal Inst Engn & Technol Dept Comp Sci & Engn Sangrur Punjab India

Road network plays a significant role in today's urban development. These are a vital part of applications such as automatic road navigation, traffic management, route optimization, etc. In this paper, we aim to explore the potential and performance of convolution neural network architecture, U-Net, performing semantic segmentation to detect roads. The model has been evaluated on Massachusetts Roads Dataset with the estimation of numerous parameters such as filter stride, learning rate, training epochs, data size, and various augmentation techniques. The employment of the proposed technique and hyperparameters to the U-Net architecture gives a boost to test accuracy and improves the dice coefficient by a value of 0.007. Experimental results thereby demonstrate that our model is computationally efficient and achieves comparable segmentation results.

关键词： Convolutional neural network (CNN) Road extraction Satellite images Fully convolutional networks Semantic image segmentation encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students

引用

IEEE ACCESS 2022年 10卷 35061-35072页

作者： Chen, Binbin Zhang, Jingyu Qiannan Normal Univ Nationalities Sch Int Studies Duyun 558000 Guizhou Peoples R China Xian Int Studies Univ English Literature Inst Xian 710128 Shaanxi Peoples R China

Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students' sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students' grammatical error correction or in a common grammatical error correction shared task.

关键词： Task analysis Error correction Decoding Machine translation Training data Training Transformers Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training

来源：评论

学校读者我要写书评

暂无评论

Multi-table Question Answering Method Based on Correlation Evaluation and Precomputed Cube 17th

Multi-table Question Answering Method Based on Correlation E...

引用

17th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wang, Bing Wang, Chunhao Zhang, Xingpeng Zhao, Chunlan Yang, Xiaoling Guo, Kuan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Southwest Petr Univ Sch Sci Chengdu 610500 Peoples R China

ISBN: (纸本)9789819754915;9789819754922

The goal of multi-table question answering (Multi-TableQA) is to enable intelligent extraction and understanding of structured table data via natural language queries. The most commonly used autoregressive pretraining language models (PLMs) encounter problems such as noise sequence information, sequence length and numerical computation limitation. To address these issues, we propose two preprocessing steps: a correlation evaluator and a precomputed cube (TaCube). The correlation evaluator assesses the relevance between questions and table columns, discarding irrelevant columns to minimize information noise and reduce sequence length. TaCube precomputes aggregation and arithmetic operations based on the query, thereby equipping the model with rapid access to numerical computations and enhancing the PLM's numerical reasoning capabilities. Experimental results demonstrate that these preprocessing steps increase the exact match accuracy on the Spider and GeoQuery multi-table question answering datasets to 33.99% and 61.43%, respectively, outperforming existing methods.

关键词： Multi-Table Question Answering Natural Language Processing encoder-decoder Table-Question Alignment

来源：评论

学校读者我要写书评

暂无评论

Idiomatizing Python Source Code Using Different Recurrent Architectures

Idiomatizing Python Source Code Using Different Recurrent Ar...

引用

Intelligent Systems Conference

作者： Szalontai, Balazs Marton, Tamas Kukucska, Akos Pinter, Balazs Gregorics, Tibor Eotvos Lorand Univ Budapest Hungary

ISBN: (纸本)9783031664304;9783031664311

Recurrent encoder-decoder architectures have been shown to be capable of solving a wide range of sequence-to-sequence problems, such as translating text, generating summary and transforming source code. In this paper, we investigate the capabilities of recurrent encoder-decoder architectures for refactoring nonidiomatic Python code. We propose multiple possible improvements to the base recurrent architecture and investigate their impact on model performance. In order to also examine the effect that such modifications might have on each other, we train 72 different encoder-decoder architectures on the task of Python code idiomatizing. The best performing architectures are thoroughly evaluated on a real-world dataset, containing programs that were written by students. This procedure necessitates extensive manual evaluation by independent annotators. The results show that the proposed improvements can greatly increase the F1-score of the method compared to the base approach.

关键词： Deep learning Recurrent Sequence-to-sequence encoder-decoder Python Idiomatizing

来源：评论

学校读者我要写书评

暂无评论

A Robustness Improved Watermarking Scheme based on Invertible Neural Network 36

A Robustness Improved Watermarking Scheme based on Invertibl...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Xu, Yirui Mao, Yaobin Nanjing Univ Sci & Tech Sch Automat Nanjing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Image watermarking technology aims to embed secret information imperceptibly into natural images for authentication or copyright protection. Invisibility and robustness are two primary concerns for watermarking algorithms. Previous methods leveraging invertible neural networks (INNs) have achieved high security in steganography. However, they lack resilience to image modifications, leading to compromised decoding quality. Conversely, encoder-decoder frameworks excel in robustness but often compromise invisibility. In this paper, a novel end-to-end robust watermarking framework is proposed that seamlessly integrates INNs with an encoder-decoder paradigm. Additionally, an efficient channel attention (ECA) block is incorporated in the message extraction stage, enhancing watermark extraction and consequently boosting decoding robustness. Furthermore, a message feature loss is introduced to regulate the INN, thereby accelerating network convergence and enhancing performance. Extensive experiments demonstrate that our proposed scheme outperforms existing algorithms in both robustness and invisibility. When subjected to a combination of 13 noise attacks, including Gaussian noise, random cropping, and JPEG compression, our model achieves a bit error rate below 1% with an embedding information length of 64 bits. Notably, the watermarked images maintain high visual quality, with PSNR and SSIM values reaching 36dB and 0.95 respectively.

关键词： encoder-decoder digital watermarking INN robustness

来源：评论

学校读者我要写书评

暂无评论

Attention Regulation for Efficient Semantic Segmentation on Unstructured Terrain 24

Attention Regulation for Efficient Semantic Segmentation on ...

引用

24th International Conference on Control, Automation and Systems

作者： Liu, Xiujin Xue, Feng Du, Xiaoxiao Univ Michigan Dept Robot Ann Arbor MI 48109 USA

ISBN: (纸本)9798331517939;9788993215380

We present AR-Net, an efficient semantic segmentation pipeline for unstructured terrains. For applications such as autonomous navigation, it is essential to accurately and efficiently understand the unstructured scenes in outdoor and urban environments. Given RGB images as inputs, the AR-Net uses an encoder backbone to extract multi-scale features and a novel Attention-Regulation layer as part of the decoder to predict the pixel-level segmentation results for unstructured terrains. Our AR-Net model achieved superior segmentation performance and fast inference on two real-world outdoor terrain datasets. We also provide detailed ablation studies and analyses on model parameter selections.

关键词： Deep learning Semantic segmentation Attention regulation encoder-decoder Transformers Unstructured environment Terrain classification real-time

来源：评论

学校读者我要写书评

暂无评论

STRUCTURE-AWARE IN-AIR HANDWRITTEN TEXT RECOGNITION WITH GRAPH-GUIDED CROSS-MODALITY TRANSLATOR 49

STRUCTURE-AWARE IN-AIR HANDWRITTEN TEXT RECOGNITION WITH GRA...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen, Yuyan Zhao, Xing Gan, Ji Leng, Jiaxu Zhang, Yan Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Inst Brain & Intelligence Guangyang Bay Lab Chongqing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In-air handwriting as a new human-computer interaction way plays an important role in many virtual/mixed-reality applications. Existing methods for in-air handwritten text recognition (IAHTR) typically directly process handwriting trajectories with deep neural networks. However, those methods all simply learn discriminative patterns by modelling low-level relationships between adjacent points of trajectories, while completely ignoring the inherent geometric structures of characters. Instead, we propose a novel Graph-guided Cross-modality Translator for IAHTR, which further explicitly exploits the geometric structures of characters for guiding the decoding of trajectories via graph-guided cross-modality attention mechanism without introducing extra annotation costs. Experiments on benchmarks IAHEW-UCAS2016 & IAM-OnDB show that our method has achieved state-of-the-art performance for handwritten text recognition.

关键词： In-Air Handwriting Handwritten Text Recognition encoder-decoder Multi-Modality Fusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：